Deep Learning in 5G Networks Conclusion References
23 19 EIDR Umpiring Security Models for Wireless Sensor Networks 19.1 Introduction 19.2 A Review of Various Routing Protocols 19.3 Scope of Chapter 19.4 Conclusions and Future Work References
24 20 Artificial Intelligence in Wireless Communication 20.1 Introduction 20.2 Artificial Intelligence: A Grand Jewel Mine 20.3 Wireless Communication: An Overview 20.4 Wireless Revolution 20.5 The Present Times 20.6 Artificial Intelligence in Wireless Communication 20.7 Artificial Neural Network 20.8 The Deployment of 5G 20.9 Looking Into the Features of 5G 20.10 AI and the Internet of Things (IoT) 20.11 Artificial Intelligence in Software-Defined Networks (SDN) 20.12 Artificial Intelligence in Network Function Virtualization 20.13 Conclusion References
25 Index
List of Tables
1 Chapter 11Table 11.1 Experimental details.Table 11.2 Analysis on clustering accuracy vs no. of classes.Table 11.3 Performance analysis in retrieval accuracy vs no. of classes.Table 11.4 Performance on false classification ratio vs no. of classes.Table 11.5 Performance analysis on time complexity vs no. of classes.Table 11.6 Performance on clustering accuracy vs no. of terms/relations.Table 11.7 Performance analysis in retrieval accuracy vs no. of terms/relations.Table 11.8 Analysis on false classification ratio vs no. of terms/relations.Table 11.9 Performance analysis on time complexity vs no. of terms/relations.
2 Chapter 12Table 12.1 SeaOne temperature data considered during day time.Table 12.2 SeaOne temperature considered during night time.Table 12.3 SeaOne temperature data considered during day time.Table 12.4 SeaOne temperature considered during night time.Table 12.5 SeaOne temperature data considered during day time.Table 12.6 SeaOne temperature considered during night time.Table 12.7 SeaOne temperature data considered during day time.Table 12.8 SeaOne temperature considered during night time.Table 12.9 Network information in statistical analysis for sample-1.Table 12.10 Model summary for calculating relative errors in testing data.Table 12.11 Network information in statistical analysis for sample-1 at night.Table 12.12 Model summary for calculating relative errors in testing data at nig...Table 12.13 Network information in statistical analysis for sample-2.Table 12.14 Model summary for calculating relative errors in testing data at nig...Table 12.15 Case processing summary for sample 1 data obtained from Table 12.1.Table 12.16 ACF function for Table 12.1 data.Table 12.17 Case processing summary for sample 1 and Table 12.2 data.Table 12.18 ACF function for Table 12.2 data.Table 12.19 PACF function for Table 2 data.Table 12.20 Absorption loss in underwater environment for input of real time tem...Table 12.21 Transmission loss in underwater environment for input of real time t...
3 Chapter 15Table 15.1 Tools and techniques in healthcare.Table 15.2 Attributes and distinct values.
4 Chapter 19Table 19.1 Rate of production for GETUS, plain AODV, ETUS, IDSEM and EIDR.Table 19.2 Communication overhead for GETUS, plain AODV, ETUS, IDSEM and EIDR.
List of Illustrations
1 Chapter 1Figure 1.1 Reinforcement learning process.Figure 1.2 Markov process.Figure 1.3 Raw images of State.Figure 1.4 Value based learning.Figure 1.5 Policy based learning.
2 Chapter 2Figure 2.1 Growth of 5G Connections worldwide.Figure 2.2 5G Market analysis.Figure 2.3 AI in next generation networks.Figure 2.4 Service providers achieving benefits through AI.
3 Chapter 3Figure 3.1 AI in supply chain and logistics market.Figure 3.2 Growth rate ranking of AI in logistics and supply chain market ecosys...
4 Chapter 4Figure 4.1 General neural network framework for crop yield analysis [7].Figure 4.2 Reinforcement learning [5].Figure 4.3 Time series forecast of agricultural reinforcement learning [8].Figure 4.4 Deep Q Networks in RL [13].Figure 4.5 Deep Q Network for crop prediction [11].
5 Chapter 5Figure 5.1 Blockchain based inventory management in cloud.Figure 5.2 Inventory financing scenario.Figure 5.3 Blockchain example.
6 Chapter 6Figure 6.1 Plant disease identification and localization.Figure 6.2 Convolution block of proposed system.Figure 6.3 (a) Leaf which has brown spot. (b) Leaf blast with background noise. ...Figure 6.4 (a) Leaf which has identified brown spot using Yolo. (b) Identified l...
7 Chapter 7Figure 7.1 System architecture.Figure 7.2 Art module.Figure 7.3 Music module.Figure 7.4 (a) Content image (b) Style image.Figure 7.5 Transformation module.Figure 7.6 (a) VGG16 data set image. (b) VGG16 architecture.Figure 7.7 (a) L content squared-error loss function. (b) Derivative of loss wit...Figure 7.8 Working of LSTM with RNN.Figure 7.9 (a) Sample input. (b) Sample output.
8 Chapter 8Figure 8.1 6G vision [9].Figure 8.2 Wireless technologies—Overview.Figure 8.3 History of wireless technology [12].Figure 8.4 AI enabled 6G.Figure 8.5 Deep learning enabled intelligent 6G networks.Figure 8.6 Drone of IoT in 6G.Figure 8.7 Deep learning in 6G—Future [3].
9 Chapter 9Figure 9.1 Spectrum sensing requirements.Figure 9.2 Spectrum agile radio.Figure 9.3 Fundamental limits on cognitive radio.Figure 9.4 Research challenges in cooperative communication.
10 Chapter 11Figure 11.1 Architecture of proposed multimedia big retrieval system.Figure 11.2 Comparison on clustering performance vs no. of classes.Figure 11.3 Comparison on retrieval accuracy vs. no. of classes.Figure 11.4 Comparison on false classification ratio vs no. of classes.Figure 11.5 Comparison on time complexity vs no. of classes.Figure 11.6 Comparison on clustering performance vs no. of terms/relations.Figure 11.7 Comparison on retrieval accuracy vs. no. of terms/relations.Figure 11.8 Comparison on false classification ratio vs no. of terms/relations.Figure 11.9 Comparison on time complexity vs no. of terms/relations.
11 Chapter 12Figure 12.1 Multi-layer perceptron model using different depth coordinates.Figure 12.2 Multi-layer perceptron model using different depth coordinates at ni...Figure 12.3 Multi-layer perceptron model using different depth coordinates at ni...Figure 12.4 Autocorrelation function and lag for temperature